Adaptive model for rate of penetration prediction based on the dynamic correlation of influencing factors

Accurately predicting the rate of penetration (ROP) is a critical benchmark for evaluating operational efficiency in drilling operations, and it is necessary to optimize the drilling parameters and construct an accurate ROP prediction model. At present, the correlations between drilling operation parameters and the ROP are commonly evaluated using a static assessment, which overlooks dynamic changes in parameter correlations during drilling processes.

An adaptive ROP prediction model that incorporates depth-varying correlations of influential parameters is constructed. This model can automatically identify the dynamic correlations of the modeling parameters at different depths of well sections, and the optimal modeling parameters for adaptive training are selected based on the ranking of the correlation coefficients.

An analysis of 33 drilling parameters across 4,837 datasets collected from 4 wellbores in Sichuan. The comparison analysis revealed that at different well sections, the dynamic correlation coefficient of each parameter deviates significantly from the overall correlation coefficient. According to the proposed model, it can dynamically select key parameters and achieve self-update based on real-time data streams, avoiding the defect of traditional fixed-parameter models that ignore the dynamic changes of well sections.

Modeling comparison analysis revealed that in multiple rounds of prediction based on dynamic correlations, the prediction accuracy in 93% of the prediction rounds exceeded that of the overall correlation, indicating that the adaptive ROP prediction model with dynamic correlations has high application value.